The TRM Model of Potential Natural Vegetation in Mountain Forests

Reger, Birgit; Häring, Tim; Ewald, Jörg

doi:10.1007/s12224-013-9158-0

Cited by 16 publications

(12 citation statements)

References 42 publications

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“…; Reger et al. ). In some studies, the predicted vegetation type with the highest probability per location was selected (Breziecki et al.…”

Section: Introductionmentioning

confidence: 97%

“…; Reger et al. ). In summary, if more information is to be retained in an unbiased way, a transformation of probabilities to a common scale needs to be developed.…”

Section: Introductionmentioning

confidence: 97%

“…; Reger et al. ) and maps showing the most likely PNV type at any given location. However, there are two reasons why this approach may lead to oversimplification.…”

Section: Introductionmentioning

confidence: 99%

“…This critique was partly due to misunderstandings, and partly due to a lack of mathematical formulation of the concept (Loidi et al 2010;Somodi et al 2012). Recently, several papers have presented PNV estimations using formal methods (Hemsing & Bryn 2012;Fischer et al 2013;Attorre et al 2014;Reger et al 2014) and maps showing the most likely PNV type at any given location. However, there are two reasons why this approach may lead to oversimplification.…”

Section: Introductionmentioning

confidence: 99%

“…One possible reason is that the synthesis of predictions of individual vegetation types is a major challenge. So far, PNV models used a variety of methods to achieve a single outcome per location; including methods that generate inherently categorical outcomes (Fischer 1990;Fischer et al 2013), the determination of thresholds to consider a type as "present" (Bittner et al 2011), or rules for choosing one type as "present" from probabilistic predictions (Hemsing & Bryn 2012;Attorre et al 2014;Reger et al 2014). In some studies, the predicted vegetation type with the highest probability per location was selected (Breziecki et al 1993;Tichy 1999;Liu et al 2009).…”

Section: Introductionmentioning

confidence: 99%

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Implementation and application of multiple potential natural vegetation models – a case study of Hungary

Somodi

Molnár

Czúcz

et al. 2017

J Vegetation Science

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QuestionsMultiple Potential Natural Vegetation (MPNV) is a framework for the probabilistic and multilayer representation of potential vegetation in an area. How can an MPNV model be implemented and synthesized for the full range of vegetation types across a large spatial domain such as a country? What additional ecological and practical information can be gained compared to traditional Potential Natural Vegetation (PNV) estimates? Location Hungary MethodsMPNV was estimated by modelling the occurrence probabilities of individual vegetation types using gradient boosting models (GBM). Vegetation data from the Hungarian Actual Habitat Database (MÉTA) and information on the abiotic background (climatic data, soil characteristics, hydrology) were used as inputs to the models. To facilitate MPNV interpretation a new technique for model synthesis (rescaling) enabling comprehensive visual presentation (synthetic maps) was developed which allows for a comparative view of the potential distribution of individual vegetation types. ResultsThe main result of MPNV modelling is a series of raw and rescaled probability maps of individual vegetation types for Hungary. Raw probabilities best suit within-type analyses, while rescaled estimations can also be compared across vegetation types. The latter create a synthetic overview of a location's PNV as a ranked list of vegetation types, and make the comparison of actual and potential landscape composition possible. For example, a representation of forest vs grasslands in MPNV revealed a high level of overlap of the potential range of the two formations in Hungary. ConclusionThe MPNV approach allows for viewing the potential vegetation composition of locations in far more detail than the PNV approach. Rescaling the probabilities estimated by the models allows easy access to the results by making potential presence of vegetation types with different data structure comparable for queries and synthetic maps. The wide range of applications identified for MPNV (conservation and restoration prioritisation, landscape evaluation) suggests that the PNV concept with the extension towards vegetation distributions is useful both for research and applications.

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“…; Reger et al. ). In some studies, the predicted vegetation type with the highest probability per location was selected (Breziecki et al.…”

Section: Introductionmentioning

confidence: 97%

“…; Reger et al. ). In summary, if more information is to be retained in an unbiased way, a transformation of probabilities to a common scale needs to be developed.…”

Section: Introductionmentioning

confidence: 97%

“…; Reger et al. ) and maps showing the most likely PNV type at any given location. However, there are two reasons why this approach may lead to oversimplification.…”

Section: Introductionmentioning

confidence: 99%